Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types
Abstract
:1. Introduction
2. Study Area and Data
3. Mapping Application for Permafrost Land Environment
3.1. Mapping Workflow, Training and Validation Experiment
3.2. Accuracy Estimates
4. Model Evaluation Results and Discussions
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Site | Sensor | Acquisition Date | Image Off Nadir | Sun Elevation | Azimuth |
---|---|---|---|---|---|
V1 | WorldView2 | 07/29/2010 | 38.6° | 35.8° | 135.5° |
V2 | WorldView2 | 07/04/2012 | 27.2° | 42.2° | 47.6° |
V3 | WorldView2 | 07/05/2015 | 15.4° | 42.4° | 203.8° |
V4 | WorldView2 | 09/03/2016 | 25.9° | 27.8° | 207.6° |
Dataset | Study Sites | Dominant Tundra | |
---|---|---|---|
Training | Russia | T1 | G1-Rush/grass, forb, cryptogam tundra G2-Graminoid, prostrate dwarf-shrub, forb tundra P1: Prostrate dwarf-shrub, herb, lichen tundra P2: Prostrate/Hemiprostrate dwarf-shrub |
Alaska | T2 | G4 Tussock-sedge, dwarf-shrub, moss tundra | |
Canada | T3 | G4:Tussock-sedge, dwarf-shrub, moss tundra G3:Non-tussock sedge, dwarf-shrub, moss tundra W2:Sedge-wetland complexes | |
Validation | Alaska | V1 | G3:Non-tussock sedge, dwarf-shrub, moss tundra W2:Sedge-wetland complexes |
V2 | G3:Non-tussock sedge, dwarf-shrub, moss tundra W2:Sedge-wetland complexes | ||
V3 | W2:Sedge-wetland complexes | ||
V4 | G4:Tussock-sedge, dwarf-shrub, moss tundra |
Validation Sites | mIoU |
---|---|
V1 | 0.91 |
V2 | 0.87 |
V3 | 0.86 |
V4 | 0.85 |
Validation Sites | Number of Reference Polygons | Correctness | Completeness | F1 Score |
---|---|---|---|---|
V1 | 582 | 0.99 | 89% | 0.96 |
V2 | 567 | 1 | 85% | 0.94 |
V3 | 579 | 1 | 83% | 0.92 |
V4 | 573 | 1 | 81% | 0.89 |
Validation Sites | Number of Reference Polygons | Correctness | Completeness | F1 Score |
---|---|---|---|---|
V1 | 582 | 0.98 | 99% | 0.97 |
V2 | 567 | 0.99 | 96% | 0.95 |
V3 | 579 | 0.98 | 97% | 0.96 |
V4 | 573 | 0.99 | 95% | 0.94 |
Validation Sites | AMRE |
---|---|
V1 | 0.17 |
V2 | 0.18 |
V3 | 0.21 |
V4 | 0.23 |
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Bhuiyan, M.A.E.; Witharana, C.; Liljedahl, A.K. Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. J. Imaging 2020, 6, 137. https://doi.org/10.3390/jimaging6120137
Bhuiyan MAE, Witharana C, Liljedahl AK. Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. Journal of Imaging. 2020; 6(12):137. https://doi.org/10.3390/jimaging6120137
Chicago/Turabian StyleBhuiyan, Md Abul Ehsan, Chandi Witharana, and Anna K. Liljedahl. 2020. "Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types" Journal of Imaging 6, no. 12: 137. https://doi.org/10.3390/jimaging6120137